Published papers

© 2019 IEEE. Wireless MIMO Switch makes the information exchange among terminals by two time steps. As a result, the highly efficiency of information exchanging is achieved. If the relay is impersonated by eavesdropper, it can exploit the information during the relay process, where the relay exploiting the information is the untrusted relay. In the proposed wireless MIMO switch, the stations whose number is the number of antennas plus one access the relay. As a result, the relay station hardly decodes each information owing to the overloaded access. In each station, firstly, one information signal is decoded by the interference cancellation of its own signal and the zero forcing of relay filter. Since one information signal is shared to all the stations, each station can demodulate the information because the overloaded condition is mitigated by the shared information signal. We confirm the effect of proposed scheme by computer simulation.

© 2019 IEEE. This paper clarifies the secure capacity in a secure information sharing among multiple destinations by using artificial noise and mirroring null steering (MNS) constructed by cooperative relays in single antenna networks. The MNS can selectively pass the desired signal to all destinations and remove artificial noise. Because the destinations archive high-quality received signals and the other systems cannot, secure information sharing can be achieved from a physical layer security perspective.

© 2019 IEEE. Similar to the deep architectures, a novel multi-layer architecture is used to extend the linear blind source separation (BSS) method to the nonlinear case in this paper. The approach approximates the nonlinearities based on a polynomial network, where the layer of our network begins with the polynomial of degree 1, up to build an output layer that can represent data with a small bias by a good approximate basis. Relying on several transformations of the input data, with higher-level representation from lower-level ones, the networks are to fulfill a mapping implicitly to the high-dimensional space. Once the polynomial networks are built, the coefficient matrix can be estimated by solving an l1-regularization on the coding coefficient vector. The experiment shows that the proposed approach exhibits a higher separation accuracy than the comparison algorithms.

© 2019 IEEE. In this paper, we divide antennas at the transmitter into K transmit antenna groups (TAGs) and extend the generalized spatial modulation (GSM) idea to a new precoding aided multiple-input multiple-output (MIMO) system, which is referred to as precoding aided GSM (PGSM) system. To select the receive antenna subset with low complexity, a class of efficient receive antenna selection techniques with low computational complexity are devised. To reduce the complexity of maximum likelihood (ML) detector, a low complexity iterative greedy (IG) algorithm relied ML (IG-ML) detector is also derived. Simulation results show that the bit error rate (BER) performance gain up to 3.5 dB is obtained by the proposed PGSM with IG-ML detector over the PGSM with ordered block minimum mean-squared error (OBMMSE) detector at a given BER of 10-4. It is also shown that the proposed PGSM with IG-ML detector achieves a similar BER performance in high signal-to-noise power ratio (SNR) region compared to that of an ideal precoding for the GSM with optimal ML detector.

© 2018 IEEE. Rate control technology could adaptively adjust the bit rate of video according to the current throughput, which could better solve the video playout interruption. The playout threshold of video playout buffer influences the overflow and underflow probabilities of the video playout and result in video playout interruption or video frame skip. In this paper we propose an optimization method that considering Quality of Experience (QoE), joint bit rate and playout threshold control under Long Term Evolution (LTE) network. An analytical framework is presented to investigate the impact of the limited playout buffer dynamics on the wireless video QoE by analyzing the transient queue length of the buffer with the diffusion approximation method. The simulation results show that the proposed method could lower the occurrence of the video playout interruption and video frame skip under the constraints of limited resources so as to improve the QoE of video.

© 2017 IEEE. In the era of advanced computer intelligence, Internet of Things (IoT) provides fantastic services to users. However, users are suffering a severe risk of private information inference, which is caused by the leakage of motion sensory data from IoT devices. Existing works of risk assessment of motion sensor based private information inference underestimates the risk because they ignore the possibility of using advanced Computational Intelligence techniques and the variety of languages with different input methods. In this paper, we assess the risk of motion sensor based private information inference by considering the variety of languages with different input methods, advanced Computational Intelligence techniques, and reinforcement learning of personal usage habits. We collect data from real users and run simulations to provide an authentic and up-to-date risk assessment. Based on the simulation result, we discuss the risky usage actions and possible defense strategies for the Internet of Things users.

© 2018 IEEE. Heart-rate variability (HRV) is closely related with physical or mental conditions. It is rather necessary to develop remote monitoring technologies to reduce subjects' pressure, e.g., heart-rate (HR) monitoring to drivers. Recently, the contactless heartbeat detection with Doppler radar has drawn extensive attention, due to less burden on subjects. However, the received signals of Doppler radar are easily contaminated by respiration or body motion, resulting in performance degradation. In this paper, to realize robust heartbeat detection with Doppler radar, a stochastic gradient approach is first proposed to reconstruct heartbeat spectrum. By correcting the gradient of cost function constructed by recursion error, and utilizing the sparse characteristics of heartbeat spectrum, the reconstructed spectrum can be obtained with minimum deviation. Furthermore, the zero-attracting sign least mean square (ZA- SLMS) algorithm based on stochastic gradient descent (SGD) is proposed, to accomplish more stable sparse spectrum reconstruction (SSR), by quantizing the updating of recursion error. Finally, HR is estimated by spectral peak tracking consisting of peak selection and verification. Experimental results validate that the proposed method can significantly improve detection accuracy over the conventional methods, based on the measurements from 5 subjects during sitting still or typing with a laptop.

© 2017 IEEE. The wireless sensor network (WSN) plays a fundamentally important role in the realization of Internet of Things and Industry 4.0. In this article, we study the physical layer security performance of a large-scale ad hoc and homogeneous WSN. To accurately evaluate the secrecy performance of the large-scale WSN, we take into account the randomness of the interfering nodes (both in terms of the number of nodes and their respective position) based on Poisson point process in our analysis. We derive novel expressions for the average secrecy capacity and secrecy outage probability, including the effects of fading, path loss, and the network's spatial randomness. The results demonstrate the impacts of several factors such as node intensity and transmission power in large-scale WSN on secrecy performance, which should be seriously taken into consideration while designing WSNs.

© 2018 IEEE. This paper proposes the novel secure information sharing among wireless nodes by the physical layer security based on multi-antenna null steering. For secure wireless communication, the secure information like the common key of cypher should be shared among nodes before data transmission. However, in wireless adhoc network, the untrusted relay node may steal the data under relay process. In the proposed sharing, a source node and two sink nodes send the information conveying signal and the artificial noises to relay station, simultaneously for avoiding decoding the information in the untrusted relay. After amplifying and forwarding relay, the two nodes do not decode the information from the received signal without removing artificial noises. The untrusted relay constructs the null steering with two antennas for removing the artificial noise. Since the channel transfer functions through which the two artificial noises are passed are common, the null points to two sink nodes can be constructed. As a result, the two nodes can remove the artificial noises. This null steering is mirroring null steering. The computer simulation shows the effect of proposed information sharing in terms of secure capacity.

© 1967-2012 IEEE. The ever-increasing amount of data in cellular networks poses challenges for network operators to monitor the quality of experience (QoE). Traditional key quality indicators (KQIs)-based hard decision methods are difficult to undertake the task of QoE anomaly detection in the case of big data. To solve this problem, in this paper, we propose a KQIs-based QoE anomaly detection framework using semi-supervised machine learning algorithm, i.e., iterative positive sample aided one-class support vector machine (IPS-OCSVM). There are four steps for realizing the proposed method while the key step is combining machine learning with the network operator's expert knowledge using OCSVM. Our proposed IPS-OCSVM framework realizes QoE anomaly detection through soft decision and can easily fine-Tune the anomaly detection ability on demand. Moreover, we prove that the fluctuation of KQIs thresholds based on expert knowledge has a limited impact on the result of anomaly detection. Finally, experiment results are given to confirm the proposed IPS-OCSVM framework for QoE anomaly detection in cellular networks.

© 2018 IEEE. Signal restoration involves the removal or minimization of degradation such as attenuation, interference, and noise. Blind signal restoration is the process of estimating either the original signals or mixture functions from the degraded signals, without any prior information about original sources. In this paper, we present a novel approach to tackle the ill-posedness of the nonlinear blind source separation problem. The derivation of our algorithm is inspired by the idea of an efficient layer-by-layer representation to approximate the nonlinearity. Once such representations are built, a final output layer is constructed by solving a convex optimization problem. Thus, the projected data can break a nonlinear problem down into the version of generalized joint diagonalization problem in the feature space. Importantly, the parameters and forms of polynomials depend solely on the input data, which guarantee the robustness of the structure. We thus address the general problem without being restricted to any specific mixture or parametric model. Experimental results show that the proposed algorithm is able to recover the nonlinear mixture with higher separation accuracy on audio datasets from the real world.

© 2018 IEEE. In this paper, we study the performance of smart networks in terms of energy efficiency (EE) based on the internet of things (IoT) technology. Energy Harvesting (EH) from surroundings is a promising solution for compensating the limitation of batteries lifetime. Fuzzy Q-Learning algorithm has been proposed to determine the sensor movement strategy toward the dedicated power stations (PSs) as energy charger nodes for mobile sensors in order to improve network lifetime via acceleration of the battery charging process of mobile sensors. Simulation results for evaluation of the proposed strategy in term of normalized network lifetime compared to the random sensor motions, confirm the effectiveness of this approach as a practical smart EH for IoT networks.

© 2018 IEEE. Internet of Things (IoT), as a widespread growing technology which connects various heterogeneous devices of wireless sensor networks (WSNs), plays a great role in sensing, monitoring, controlling of the covering environment. However, maximizing the lifetime of WSNs is still a major challenge. Although some approaches have been introduced to overcome the vital problem so far, research on this problem experiences a slow progress. Inspired by the promising performance of fuzzy-based Q-learning (FQL) algorithm to design practical smart sensors, this paper proposes a FQL-based approach that can maximize the lifetime of sensors and accelerate the process of wireless energy harvesting (EH) for mobile sensors which coexist with macro and small base stations (SBSs) deployed over a time-variant heterogeneous network (HetNet). The methodology is based on a centralized image-processing (IP) approach to scan and find the instant coverage map of the HetNet and then to localize red regions, i.e., regions with high levels of energy. Furthermore, mobile sensors attempt to access these regions during frequent movements. This will help to maximize the lifetime of the WSN. Simulation results confirm the effectiveness of the wireless EH process and smart aggregation of mobile sensors around the dense-coverage areas.

© 2019 IEEE. Heartbeats and a blink are two major physiological signals that provide crucial information on our stress and drowsiness. Hence, it is highly demanded to detect simultaneously heartbeats and blinks in many applications. A Doppler sensor could be a key device to facilitate the non-contact heartbeat and blink detection in daily life. Although many Doppler sensor-based heartbeat and blink detection methods have been independently proposed, when heartbeats and blinks are detected simultaneously with one Doppler sensor, the detection accuracies of such heartbeat and blink detection methods get degraded because of at least two issues: (i) the low SNR (Signal-to-Noise Ratio) of each signal reflected from a subject's chest and face, and (ii) the similarity of the spectrum distribution of heartbeats and a blink. In this paper, we propose a spectrogram-based simultaneous heartbeat and blink detection using one Doppler sensor. In the proposed method, to extract the spectra that might be due to heartbeats and a blink, the spectra on a spectrogram are integrated. Blink detection is then performed by classifying the peaks of the integrated spectrum into a peak due to a blink or a non-blink based on a supervised machine learning classifier trained with a set of the time domain and the time-frequency domain features. Based on the non-blink peaks, heartbeats are detected considering the RRI (R-R Interval) estimated before the investigated peak to prevent the incorrect heartbeat detection. Through the experiments in the case where microwaves are transmitted from one Doppler sensor toward a body including a chest and a face, our proposal has been shown to be able to simultaneously detect heartbeats and blinks with high accuracy.

© 2002-2012 IEEE. Future air-ground integrated vehicular network (AGVN) is a vital part of the expected ubiquitous communication system. The resources in AGVN are geographically separated and are often heterogeneous. Therefore, distributed management and cooperative management are key issues in a multi-tier based topology of AGVN. Recently, artificial intelligence (AI) shows advantages in facilitating the management of AGVN; however, adequate, real-time, and secure information for driving AI is still a big concern. In this article, we propose a conceptual enhanced AGVN by introducing a so-called surveillance plane to promote the management in the control plane. Thus, components such as virtual network functions (VNFs) and service function chaining (SFC) can get profits. Concepts and trials of utilizing the surveillance plane and side information are presented from three different aspects, namely networking, security, and application. We also summarize research directions in designing a more flexible management scheme by coupling the service, surveillance, and control planes of the enhanced AGVN.

© 2018 IEEE. Due to the increased popularity of Bitcoin, many researchers have analyzed how Bitcoin is being used based on the transaction history. However, the existing works analyze the transaction history in a 'static' manner and none of them analyzes transaction history 'dynamically', i.e. without taking into account the 'time variation of how Bitcoin is transferred'. The time analysis is in great demand for many practical cases, such as digital forensics tool that infers what was going on behind the scene of a fraudulent scam, and real-time inference of marketplace sales. In this paper, we propose a novel time series analysis for analyzing the history of Bitcoin transactions. In fact the main goal of our research is to detect changing points, namely anomaly detection, against a given (Bitcoin) address's transaction history. To show the effectiveness of the proposed approach, it is tested against the transaction history of Pirate@40's HYIP (High Yielding Investment Program) scheme, which raised 700,000 BTC from his investors and was charged by the Security and Exchange Commission (SEC) in 2013. It is shown that the proposed approach can successfully detect several remarkable points of Pirate@40's HYIP scheme, such as when its program's name was changed to Bitcoin Saving & Trust and when its investment rule was changed.

© 2011 IEEE. Automatic modulation classification (AMC) is an essential technology for the non-cooperative communication systems, and it is widely applied into various communications scenarios. In the recent years, deep learning (DL) has been introduced into AMC due to its outstanding identification performance. However, it is almost impossible to implement previously proposed DL-based AMC algorithms without large number of labeled samples, while there are generally few labeled sample and large unlabel samples in the realistic communication scenarios. In this paper, we propose a transfer learning (TL)-based semi-supervised AMC (TL-AMC) in a zero-forcing aided multiple-input and multiple-output (ZF-MIMO) system. TL-AMC has a novel deep reconstruction and classification network (DRCN) structure that consists of convolutional auto-encoder (CAE) and convolutional neural network (CNN). Unlabeled samples flow from CAE for modulation signal reconstruction, while labeled samples are fed into CNN for AMC. Knowledge is transferred from the encoder layer of CAE to the feature layer of CNN by sharing their weights, in order to avoid the ineffective feature extraction of CNN under the limited labeled samples. Simulation results demonstrated the effectiveness of TL-AMC. In detail, TL-AMC performs better than CNN-based AMC under the limited samples. What's more, when compared with CNN-based AMC trained on massive labeled samples, TL-AMC also achieved the similar classification accuracy at the relative high SNR regime.

© 2018 IEEE. Relay network assisted wireless terminal has an advantage for easily constructing wireless network but a disadvantage for information leak to the wireless terminal. For secure wireless communication, an encryption but the sharing of secrete key between information source and destination are required. A secure information sharing with artificial noise and mirroring null steering for avoiding the information leak to untrusted relay has been proposed. In this paper, for enhancing the security of this proposed secure information sharing, we consider two transmit controls, transmit power control (TPC) and the adaptive node selection of information source. We evaluate the secure capacity by computer simulation.

© 2019 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, i.e., nonlinear, underdetermined mixture, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-layer representation and sparse representation in time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an l1-regularization on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture TF vectors with a fewer computational cost. The experiments are run on the observations that are generated from nonlinear functions, and that are collected with some direction angles in a virtual room environment. The proposed approach exhibits a higher separation accuracy than that of the conventional algorithms.

© 2013 IEEE. Blind source separation (BSS) is a technique to recognize the multiple talkers from the multiple observations received by some sensors without any prior knowledge information. The problem is that the mixing is always complex, such as the case where sources are mixed with some direction angles, or where the number of sensors is less than that of sources. In this paper, we propose a multi-subspace representation based BSS approach that allows the mixing process to be nonlinear and underdetermined. The approach relies on a multi-subspace structure and sparse representation in the time-frequency (TF) domain. By parameterizing such subspaces, we can map the observed signals in the feature space with the coefficient matrix from the parameter space. We then exploit the linear mixture in the feature space that corresponds to the nonlinear mixture in the input space. Once such subspaces are built, the coefficient matrix can be constructed by solving an optimization problem on the coding coefficient vector. Relying on the TF representation, the target matrix can be constructed in a sparse mixture of TF vectors with the fewer computational cost. The experiments are designed on the observations that are generated from an underdetermined mixture, and that is collected with some direction angles in a virtual room environment. The proposed approach exhibits higher separation accuracy.